The ability to predict which oral precancer will eventuate to cancer will reduce the mortality and morbidity of this dreadful cancer which accounts for 38,000 new cancer cases in the United States and 350,000 worldwide. Only 20% of oral precancers progress to cancer and currently there is no clinical, morphological nor molecular parameter that can predict the malignant progression of oral precancers. This proposal aims to harness the molecular determinants of oral precancer progression using genomic, transcriptional and proteomic approaches on patient population that will permit the maximal utility of emerging technologies to address this critical research and biological question in head and neck cancer. The hypothesis to be tested is that oral precancer that eventuates to cancer harbor genomic, transcriptional and proteomic alterations which serves to advance the pathogenesis as well as valuable biomarkers of this disease. Patient resources are in place to permit genomic, transcriptional and proteomic studies including longitudinal cohorts of oral precancer patients with clinical outcome (Aim 1). Bioinformatics and biocomputational expertise are in place to harness diagnostic molecular determinants in the progressing oral precancers and to build Prediction Models for Oral Precancer Progression (PMOPP) (Aim 2). Submodels to use saliva and serum as a diagnostic fluid for oral cancer detection will also be generated. Aim 3 is to test the predictor models in a multi-center setting to evaluate the clinical utility of the oral precancer prediction models. We envision to generate a non-invasive prediction model, based on saliva and/or serum, for the early detection of progressing oral precancer. This proposal optimally utilize existing patient resources and emerging genomic, transcriptional and Proteomic tools to harness genomic, transcriptional and proteomic determinants in oral precancer progression. The outcome of the validated molecular determinants will be utilized for biological mechanistic studies as well as translational research applications, directions that are well in place in our laboratories. [unreadable] [unreadable]